DemonstrationsAtomicFlow.py

import jinja2
from flows.base_flows import AtomicFlow
from flows.utils import logging
from flows.utils import general_helpers
from typing import Dict,Any,Optional,List
from flows.prompt_template import JinjaPrompt
from copy import deepcopy
import os
import hydra
log = logging.get_logger(__name__)

class DemonstrationsAtomicFlow(AtomicFlow):
    """ This class implements a Demonstrations Atomic Flow. It is a flow which is usually used to pass demonstrations (of user assistant interactions)
    to the ChatAtomicFlow.
    
    *Configuration Parameters*:
    
    - `name` (str): The name of the flow. Default: "DemonstrationsAtomicFlow"
    - `description` (str): A description of the flow. This description is used to generate the help message of the flow.
    Default: "A flow that passes demonstrations to the ChatFlow"
    - `data` (List[Dict[str, Any]]): The data of the demonstrations.
    If data is None, the data is loaded from the file specified in the params["data_dir"].
    Default: No default value this field must be set.
    - `params` (Dict[str, Any]): The parameters specific to the dataset of the demonstrations. Its default parameters are:
        - `data_dir` (str): The directory where the demonstrations are stored. If the data is not directly passed to the flow through `data` then
        the data is loaded from this directory. Default: No default value this field must be set.
        - `demonstrations_id` (str): The id of the demonstrations (name of the data file). If the data is not directly passed to the flow through `data` then
        the data is loaded from this file. Default: No default value this field must be set.
        - `demonstrations_k` (int): The number of demonstrations to pass to the ChatFlow.
        If None, all the demonstrations are passed to the ChatFlow. Default: None
    - `query_prompt_template` (Dict[str, Any]): The prompt template used to generate the query of the demonstrations. 
    By default its of type flows.prompt_template.JinjaPrompt. None of the parameters of the prompt are defined by default and therefore need to be defined if one
    wants to use the query_prompt_template. Default parameters are defined in flows.prompt_template.jinja2_prompts.JinjaPrompt.
    - `response_prompt_template` (Dict[str, Any]): The prompt template used to generate the response of the demonstrations. By default its of type flows.prompt_template.JinjaPrompt.
    None of the parameters of the prompt are defined by default and therefore need to be defined if one
    wants to use the response_prompt_template. Default parameters are defined in flows.prompt_template.jinja2_prompts.JinjaPrompt.

    *Input Interface*:
    
    - The input interface expected by its successor flow (e.g. typically ChatAtomicFlow so the input interface is the one expected by ChatAtomicFlow)
        
    *Output Interface*:

    - The input interface expected by its successor flow (e.g. typically ChatAtomicFlow so the input interface expected by ChatAtomicFlow))
    - `demonstrations` (List[Dict[str, Any]]): A list of demonstrations. Each demonstration is a dictionary with the following keys:
                                                    - idx (int): The index of the demonstration
                                                    - query (str): The query of the demonstration
                                                    - response (str): The response of the demonstration
        
    :param params: The parameters specific to the dataset of the demonstrations. It must sould contain the following keys:
                        - 'data_dir' (str): The directory where the demonstrations are stored. This field is used if the data is not directly passed to the flow through the 'data' field.
                        - 'demonstrations_id' (str): The id of the demonstrations (name of the data file). This field is used if the data is not directly passed to the flow through the 'data' field.
                        - 'demonstrations_k' (int): The number of demonstrations to pass to the ChatFlow. If None, all the demonstrations are passed to the ChatFlow.
                        - 'ids_to_keep' (Optional[Union[str, List[str]]]): The ids of the demonstrations to keep. If None, all the demonstrations are kept.
    :type params: Dict[str, Any]
    :param query_prompt_template: The prompt template used to generate the query of the demonstrations.
    :type query_prompt_template: JinjaPrompt
    :param response_prompt_template: The prompt template used to generate the response of the demonstrations.
    :type response_prompt_template: JinjaPrompt
    :param data: The data of the demonstrations. If None, the data is loaded from the file specified in the params.
    :type data: Optional[List[Dict[str, Any]]]
    """
    demonstrations_k: Optional[int] = None
    query_prompt_template: JinjaPrompt
    response_prompt_template: JinjaPrompt
    params: Dict
    
    def __init__(self,params,query_prompt_template,response_prompt_template, data=None,**kwargs):
        super().__init__(**kwargs)
        self.params = params
        self.data = data
        self.demonstrations_k = self.params.get("demonstrations_k", None)
        
        #typically the query would be what the user (human) asks the assistant (LLM) 
        self.query_prompt_template = query_prompt_template
        #typically the response would be what the assistant (LLM) should answer to the user (human) 
        self.response_prompt_template = response_prompt_template
        if self.data is None:
            self._load_data()
            
    @classmethod
    def _set_up_prompts(cls, config):
        """ This method instantiates the prompt templates of the flow (used when instantiating the flow from a config file)
        
            :param config: The configuration of the flow.
            :type config: Dict[str, Any]
            :return: A dictionary of keyword arguments to pass to the constructor of the flow.
            :rtype: Dict[str, Any]
        """
        kwargs = {}
        kwargs["query_prompt_template"] = \
            hydra.utils.instantiate(config['query_prompt_template'], _convert_="partial")
        kwargs["response_prompt_template"] = \
            hydra.utils.instantiate(config['response_prompt_template'], _convert_="partial")
        return kwargs
        
    @classmethod
    def instantiate_from_config(cls, config):
        """ This method instantiates the flow from a config file. 
        
        :param config: The configuration of the flow.
        :type config: Dict[str, Any]
        :return: The instantiated flow.
        :rtype: Flow
        """
        flow_config = deepcopy(config)

        kwargs = {"flow_config": flow_config}

        # ~~~ Set up prompts ~~~
        kwargs.update(cls._set_up_prompts(flow_config))
        kwargs.update({"params": flow_config["params"]})
        kwargs.update({"data": flow_config["data"]})
        # ~~~ Instantiate flow ~~~
        return cls(**kwargs)
            
    def _get_query_message_content(self, sample_data: Dict):
        """ This method returns the query message content of a demonstration given the sample data (by rendering the query prompt template).
        
        :param sample_data: The sample data of the demonstration.
        :type sample_data: Dict[str, Any]
        :return: The query message content of the demonstration.
        :rtype: str
        """
        input_variables = self.query_prompt_template.input_variables
        return self.query_prompt_template.format(**{k: sample_data[k] for k in input_variables})

    def _get_response_message_content(self, sample_data: Dict):
        """ This method returns the response message content of a demonstration given the sample data (by rendering the response prompt template).
        
        :param sample_data: The sample data of the demonstration.
        :type sample_data: Dict[str, Any]
        :return: The response message content of the demonstration.
        :rtype: str
        """
        input_variables = self.response_prompt_template.input_variables
        return self.response_prompt_template.format(**{k: sample_data[k] for k in input_variables})
        
    def _get_io_pair(self, idx):
        """ This method, given the index of a demonstration, returns an query-response pair from the demonstrations data.
        
        :param idx: The index of the demonstration.
        :type idx: int
        :return: The query-response pair at idx from the demonstrations data.
        :rtype: Dict[str, Any]
        """
        dp = self.data[idx]
        
        query_data = dp["query_data"]
        response_data = dp["response_data"]
        
        query = self._get_query_message_content(query_data)
        response = self._get_response_message_content(response_data)
        
        return {"idx": idx, "query": query,"response": response}
            
    def _get_io_pairs(self,input_data: Dict[str, Any]) -> List[Any]:
        """ This method returns the demonstrations that are passed to the destination flow (typically ChatAtomicFlow).
        
        :param input_data: The input data of the flow.
        :type input_data: Dict[str, Any]
        :return: The demonstrations that are passed to the destination flow.
        :rtype: List[Any]
        """
        demonstrations_k = self.demonstrations_k if self.demonstrations_k is not None else len(self.data)
        io_pairs = [self._get_io_pair(idx) for idx in range(demonstrations_k)]
        return io_pairs
        
    def _load_data(self):
        """ This method loads the demonstrations from the file specified in the params. It also filters the demonstrations if the ids_to_keep parameter is specified."""
        demonstrations_file = os.path.join(self.params["data_dir"], f"{self.params['demonstrations_id']}.jsonl")
        self.data = general_helpers.read_jsonlines(demonstrations_file)
        
        if self.params.get("ids_to_keep", False):
            if isinstance(self.params["ids_to_keep"], str):
                ids_to_keep = set(self.params["ids_to_keep"].split(","))
            else:
                ids_to_keep = set(self.params["ids_to_keep"])

            self.data = [d for d in self.data if d["id"] in ids_to_keep]

        log.info("Loaded the demonstrations for %d datapoints from %s", len(self.data), self.params["data_dir"])

    def run(self,
            input_data: Dict[str, Any]) -> Dict[str, Any]:
        """ This method runs the flow. It returns the input data of the flow with the demonstrations added to it.
        
        :param input_data: The input data of the flow.
        :type input_data: Dict[str, Any]
        :return: The input data of the flow with the demonstrations added to it.
        :rtype: Dict[str, Any]
        """
        return {**input_data,**{"demonstrations": self._get_io_pairs(input_data=input_data)}}